Apparatus for high frequency trading and method of operating thereof

ABSTRACT

The disclosure relates to an apparatus for high frequency trading. The apparatus includes one or more memories, at least one reconfigurable processor coupled to the one or more memories, and a dedicated accelerator preconfigured for the machine learning model. The one or more processors receive market-related information from one or more market-related information servers and generates market prediction reference data based on the market-related information. The dedicated accelerator performs operations for the machine learning model with the market prediction reference data to generate future market prediction data. The at least one reconfigurable processor generates an order signal based on the future market prediction data and transmits the order signal to a target exchange server.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2021-0158053, filed in the Korean IntellectualProperty Office on Nov. 16, 2021, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to an apparatus for high frequencytrading and a method of operating thereof, and more specifically, butnot limited to, an apparatus for generating orders for high frequencytrading using an accelerator for a machine learning model and at leastone processor capable of reprogramming and/or design change, and amethod of operating thereof.

BACKGROUND

High frequency trading is a trading method of trading with highfrequency (e.g., hundreds to thousands of times per second) within ashort period of time by using minute price variations of securities suchas stocks, bonds, derivatives, etc. For the high frequency trading, fastprocessing speed is very important. In general, the shorter the time ittakes to process the trading algorithms based on the input informationand output the result, the more advantageous it can get in trading.

Meanwhile, high frequency trading using machine learning models has anadvantage of being able to find features that the traditional algorithmscannot find from the complex information in the market. However, inorder to store and process the computations of the machine learningmodel, a processor having large storage spaces and operational resourcesis required, but there is a problem in that the processor used for highfrequency trading generally does not have these resources.

In addition, there is a problem in that a processor suitable for drivinga machine learning model is not suitable for performing a pre-processingprocess or a post-processing process which may be frequently changedaccording to market conditions, because the greater flexibility willlead to a lower efficiency.

The description set forth in the background section should not beassumed to be prior art merely because it is set forth in the backgroundsection. The background section may describe aspects or embodiments ofthe present disclosure.

SUMMARY

In order to solve the problems described above, the present disclosureprovides an apparatus for high frequency trading, an operating methodthereof, and a non-transitory computer-readable recording medium storinginstructions.

The present disclosure may be implemented in a variety of ways,including a method, an apparatus (system), or a non-transitorycomputer-readable storage medium storing instructions.

An apparatus for high frequency trading according to an embodiment ofthe present disclosure may comprises one or more memories, at least onereconfigurable processor coupled to the one or more memories, the one ormore processors configured to receive market-related information fromone or more market-related information servers and generate marketprediction reference data based on the market-related information, adedicated accelerator preconfigured for the machine learning model, thededicated accelerator configured to receive the market predictionreference data, perform operations for the machine learning model togenerate future market prediction data, and provide the future marketprediction data to the at least one reconfigurable processor, whereinthe at least one reconfigurable processor is further configured togenerate an order signal based on the future market prediction data andtransmit the order signal to a target exchange server.

According to an embodiment, the at least one reconfigurable processormay be implemented as a field programmable gate array (FPGA), and thededicated accelerator may be implemented as an integrated circuit for aneural processing unit (NPU).

According to an embodiment, the one or more market-related informationservers may include at least one of one or more reference exchangeservers, one or more news providing servers, one or more social networkservice (SNS) servers, or one or more online shopping service servers.

According to an embodiment, the at least one reconfigurable processormay be further configured to determine a prediction complexity based onthe market prediction reference data, and determine an appropriate wayaccording to the determined prediction complexity. If it is determinedthat the appropriate way is a predetermined rule, the at least onereconfigurable processor may be further configured to generate the ordersignal according to the predetermined rule based on the marketprediction reference data. If it is determined that the appropriate wayis the machine learning model, the dedicated accelerator may be furtherconfigured to perform operations for the machine learning model togenerate the future market prediction data and provide the future marketprediction data to the at least one reconfigurable processor so that theat least one reconfigurable processor generates the order signal basedon the future market prediction data.

According to an embodiment, the apparatus may further comprise a hostdevice configured to drive a trading engine, wherein the predictioncomplexity may include three complexity classes according to thecomplexity, and the market prediction reference data may be provided toat least one of the at least one reconfigurable processor, the hostdevice or the dedicated accelerator for the machine learning modelaccording to the three complexity classes to generate the order signal.

According to an embodiment, the at least one reconfigurable processormay be further configured to parse and decode the market-relatedinformation, and generate the market prediction reference data based onthe parsed and decoded market-related information.

According to an embodiment, wherein the market prediction reference datamay include one or more reference features for one or more referenceitems at one or more time points.

According to an embodiment, the one or more reference items may includea reference item representing a leading indicator, and a target item tobe ordered.

According to an embodiment, the at least one reconfigurable processormay be further configured to process the generated order signalaccording to a protocol required by the target exchange server.

According to an embodiment, the market-related information may includeinformation on an order book of one or more reference items in areference exchange associated with a reference exchange server, and aresponse to a previous order in the target exchange associated with thetarget exchange server.

According to an embodiment, a method of operating an apparatus for highfrequency trading including at least one reconfigurable processor maycomprise receiving, by the at least one reconfigurable processor,market-related information from one or more market-related informationservers, generating, by the at least one reconfigurable processor,market prediction reference data based on the market-relatedinformation, transmitting, by the at least one reconfigurable processor,the market prediction reference data to a dedicated acceleratorpreconfigured for the machine learning model and configured to performoperations of the machine learning model with the market predictionreference data to generate future market prediction data, receiving, bythe at least one reconfigurable processor, the future market predictiondata from the dedicated accelerator, generating, by the at least onereconfigurable processor, an order signal based on the future marketprediction data and transmitting the order signal to a target exchangeserver.

According to an embodiment, the one or more market-related informationservers may include at least one of one or more reference exchangeservers, one or more news providing servers, one or more social networkservice (SNS) servers, or one or more online shopping service servers.

According to an embodiment, the method may further comprise determining,by the at least one reconfigurable processor, a prediction complexitybased on the market prediction reference data, determining, by the atleast one reconfigurable processor, an appropriate way according to thedetermined prediction complexity, if it is determined that theappropriate way is a predetermined rule, generating, by the at least onereconfigurable processor, the order signal according to thepredetermined rule based on the market prediction reference data, and ifit is determined that the appropriate way is the machine learning model,transmitting, by the at least one reconfigurable processor, the marketprediction reference data to the dedicated accelerator configured toperform operations of the machine learning model operations for themachine learning model with the market prediction reference data togenerate future market prediction data.

According to an embodiment, the prediction complexity may include threecomplexity classes according to the complexity, and the marketprediction reference data may be provided to at least one of the atleast one reconfigurable processor, a host device or the NPU accordingto the three complexity classes to generate the order signal.

According to an embodiment, the method may further comprise parsing anddecoding, by the at least one reconfigurable processor, themarket-related information, and wherein generating the market predictionreference data may comprise generating the market prediction referencedata based on the parsed and decoded market-related information.

According to an embodiment, the market prediction reference data mayinclude one or more reference features for one or more reference itemsat one or more time points.

According to an embodiment, the one or more reference items may includea reference item representing a leading indicator, and a target item tobe ordered.

According to an embodiment, the method may further comprise processing,by the at least one reconfigurable processor, the generated order signalaccording to a protocol required by the target exchange server.

According to an embodiment, the market-related information may includeinformation on an order book of one or more reference items in areference exchange associated with a reference exchange server, and aresponse to a previous order in the target exchange associated with thetarget exchange server.

According to an embodiment, an apparatus for high frequency trading maycomprise one or more memories, at least one reconfigurable processorcoupled to the one or more memories, the one or more processorsconfigured to cause receiving market-related information from one ormore market-related information servers, generating market predictionreference data based on the market-related information, transmitting themarket prediction reference data to a dedicated acceleratorpreconfigured for the machine learning model and configured to performoperations of the machine learning model with the market predictionreference data to generate future market prediction data, receiving thefuture market prediction data from the dedicated accelerator, generatingan order signal based on the future market prediction data, andtransmitting the order signal to a target exchange server.

According to some embodiments of the present disclosure, by using amachine learning model, it is possible to find features that classicalalgorithms cannot find from complex market conditions, and also predictfuture market conditions and use this to generate orders to gain anadvantage in trading.

According to some embodiments of the present disclosure, the processorrunning the machine learning model may be configured with a dedicatedaccelerator (e.g., NPU ASIC) to process the operations of the machinelearning model quickly and efficiently, and the pre/post-processing canbe flexibly changed in accordance with the changed market conditions byusing a processor capable of reprogramming or re-designing (e.g., FPGA).In this way, by using two or more different processors, it is possibleto simultaneously achieve both the implementation of flexiblepre/post-processing and efficient and fast arithmetic processing ofmachine learning models.

According to some embodiments of the present disclosure, by determiningthe prediction complexity based on the input data, it is possible togenerate order-related data by using the machine learning model when theprediction complexity is high so as to find the features that classicalalgorithms cannot find and also predict future market conditions, andgenerate the order data directly by using relatively simple tradinglogic when the prediction complexity is low so as to quickly transmitorders and gain an edge in trading.

According to some embodiments of the present disclosure, in the device(e.g., FPGA), by performing a series of processes including processingthe market data and using the result to generate order data, andtransmitting an order, latency can be minimized.

The effects of the present disclosure are not limited to the effectsdescribed above, and other effects not described herein can be clearlyunderstood by those of ordinary skill in the art (referred to as“ordinary technician”) from the description of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentdisclosure will become more apparent to those of ordinary skill in theart by describing in detail exemplary embodiments thereof with referenceto the accompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating an operation example of ahigh frequency trading apparatus according to an embodiment of thepresent disclosure;

FIG. 2 is a block diagram illustrating internal components of a highfrequency trading apparatus according to an embodiment of the presentdisclosure;

FIG. 3 is a diagram illustrating internal components of a processoraccording to an embodiment of the present disclosure;

FIG. 4 is a diagram illustrating internal components of a processorincluding a complexity determining unit that determines predictioncomplexity according to an embodiment of the present disclosure;

FIG. 5 is a diagram illustrating an example of a method of determining aprediction complexity and determining whether or not to use a machinelearning model according to the determined prediction complexity,according to an embodiment of the present disclosure;

FIG. 6 is a diagram illustrating an example of a method for generatinghigh frequency orders according to an embodiment of the presentdisclosure;

FIG. 7 is a diagram illustrating an example in which a machine learningmodel outputs future market prediction data based on market predictionreference data according to an embodiment of the present disclosure;

FIG. 8 is a diagram illustrating an example of a configuration of marketprediction reference data for a machine learning model according to anembodiment of the present disclosure;

FIG. 9 is a flowchart illustrating an example of a method of operating ahigh frequency trading apparatus including an FPGA and an NPU accordingto an embodiment of the present disclosure;

FIG. 10 is a flowchart illustrating an example of a method forgenerating high frequency orders according to an embodiment of thepresent disclosure;

FIG. 11 illustrates an example of an artificial neural network modelaccording to an embodiment of the present disclosure; and

FIG. 12 is a block diagram of any computing device associated with highfrequency trading or generation of high frequency orders according to anembodiment of the present disclosure.

FIG. 13 is a ladder diagram illustrating an example signal exchangeaccording to an embodiment of the present disclosure.

DETAILED DESCRIPTION

Hereinafter, specific details for the practice of the present disclosurewill be described in detail with reference to the accompanying drawings.However, in the following description, detailed descriptions ofwell-known functions or configurations will be omitted when it may makethe subject matter of the present disclosure rather unclear.

In the accompanying drawings, the same or corresponding elements areassigned the same reference numerals. In addition, in the followingdescription of the embodiments, duplicate descriptions of the same orcorresponding components may be omitted. However, even if descriptionsof components are omitted, it is not intended that such components arenot included in any embodiment.

Advantages and features of the disclosed embodiments and methods ofaccomplishing the same will be apparent by referring to embodimentsdescribed below in connection with the accompanying drawings. However,the present disclosure is not limited to the embodiments disclosedbelow, and may be implemented in various forms different from eachother, and the present embodiments are merely provided to make thepresent disclosure complete, and to fully disclose the scope of theinvention to those skilled in the art to which the present disclosurepertains.

The terms used herein will be briefly described prior to describing thedisclosed embodiments in detail. The terms used herein have beenselected as general terms which are widely used at present inconsideration of the functions of the present disclosure, and this maybe altered according to the intent of an operator skilled in the art,conventional practice, or introduction of new technology. In addition,in specific cases, certain terms may be arbitrarily selected by theapplicant, and the meaning of the terms will be described in detail in acorresponding description of the embodiments. Therefore, the terms usedin the present disclosure should be defined based on the meaning of theterms and the overall content of the present disclosure rather than asimple name of each of the terms.

As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesthe singular forms. Further, the plural forms are intended to includethe singular forms as well, unless the context clearly indicates theplural forms. Further, throughout the description, when a portion isstated as “comprising (including)” a component, it intends to mean thatthe portion may additionally comprise (or include or have) anothercomponent, rather than excluding the same, unless specified to thecontrary.

Further, the term “module” or “unit” used herein refers to a software orhardware component, and “module” or “unit” performs certain roles.However, the meaning of the “module” or “unit” is not limited tosoftware or hardware. The “module” or “unit” may be configured to be inan addressable storage medium or configured to reproduce one or moreprocessors. Accordingly, as an example, the “module” or “unit” mayinclude components such as software components, object-oriented softwarecomponents, class components, and task components, and at least one ofprocesses, functions, attributes, procedures, subroutines, program codesegments of program code, drivers, firmware, micro-codes, circuits,data, database, data structures, tables, arrays, and variables.Furthermore, functions provided in the components and the “modules” or“units” may be combined into a smaller number of components and“modules” or “units”, or further divided into additional components and“modules” or “units.”

According to an embodiment, the “module” or “unit” may be implemented asa processor and a memory. The “processor” should be interpreted broadlyto encompass a general-purpose processor, a central processing unit(CPU), a microprocessor, a digital signal processor (DSP), a controller,a microcontroller, a state machine, and so forth. Under somecircumstances, the “processor” may refer to, comprise, be implementedas, or be included in an application-specific integrated circuit (ASIC),a programmable logic device (PLD), a field-programmable gate array(FPGA), and so on. The “processor” may refer to a combination ofprocessing devices, e.g., a combination of a DSP and a microprocessor, acombination of a plurality of microprocessors, a combination of one ormore microprocessors in conjunction with a DSP core, or any othercombination of such configurations. In addition, the “memory” should beinterpreted broadly to encompass any electronic component that iscapable of storing electronic information. The “memory” may refer tovarious types of processor-readable media such as random access memory(RAM), read-only memory (ROM), non-volatile random access memory(NVRAM), programmable read-only memory (PROM), erasable programmableread-only memory (EPROM), electrically erasable PROM (EEPROM), flashmemory, magnetic or optical data storage, registers, and so on. Thememory is said to be in electronic communication with a processor if theprocessor can read information from and/or write information to thememory. The memory integrated with the processor is in electroniccommunication with the processor.

In the present disclosure, “system” may refer to at least one of aserver device or a cloud device, but not limited thereto. For example,the system may include one or more server devices. In another example,the system may include one or more cloud devices. In still anotherexample, the system may include both the server device and the clouddevice operated in conjunction with each other.

In the present disclosure, the “machine learning model” may include anymodel that is used for inferring an answer to a given input. Accordingto an embodiment, the machine learning model may comprise, refer to, orbe implemented as an artificial neural network (ANN), a deep neuralnetwork (DNN), a convolutional neural network (CNN), a recurrent neuralnetwork (RNN), a generative adversarial network (GAN), or a combinationof some or all of the foregoing. For example, the ANN may include aninput layer, a plurality of hidden layers, and an output layer. Eachlayer may include a plurality of nodes.

In the present disclosure, “display” may refer to, but not limited to,any display device associated with a computing device, and for example,it may refer to any display device that is controlled by the computingdevice, or that can display any information/data provided from thecomputing device.

In the present disclosure, “each of a plurality of A” may refer to eachof all components included in the plurality of A, or may refer to eachof some of the components included in a plurality of A.

The phrases “A, B, or C,” “at least one of A, B, and C,” or “at leastone of A, B, or C” may refer to only A, only B, only C, or anycombination of A, B, and C.

In the present disclosure, the term “item”, “tradable item” or“security” may refer to, but not limited to, any form of tradablefinancial assets such as stocks, bonds, derivatives (options, futures,etc.) index-related items, industrial sector-related items, items forspecific commodities (e.g., crude oil, agricultural products, gold,etc.), exchange rate-related items, cryptocurrencies, etc.

In the present disclosure, a “exchange server” may refer to, but notlimited to, a system through which brokers and traders can buy and selltradable items such as securities circulated in at least one country

In the present disclosure, an “Order Book (OB)” may, but not limited to,include a list in which information on buy or sell orders (ask price,quantity, information on buyers or sellers, etc.) of buyers and sellersexisting in the securities market is recorded.

In the present disclosure, the “Top of the Book (ToB)” may include, butnot limited to, information on the highest bid price and lowest bidprice.

FIG. 1 is a schematic diagram illustrating an operation example of ahigh frequency trading apparatus according to an embodiment of thepresent disclosure.

As shown in FIG. 1 , a high frequency trading apparatus 110, one or moremarket-related information servers 120, and a target exchange server 130may be involved for the high frequency trading according to embodimentsof the present disclosure.

The high frequency trading apparatus 110 may collect market-relatedinformation from one or more market-related information servers 120. Thehigh frequency trading apparatus 110 may generate an order signalordering a tradable item with a specific condition based on thecollected market-related information and send the order signal to thetarget exchange server 130.

The one or more market-related information servers 120 may include atleast one of one or more reference exchange servers, one or more newsproviding servers, one or more social network service (SNS) servers, orone or more online shopping service servers. The one or more referenceexchange servers may be associated with one or more reference exchange,respectively. Each reference exchange server of the one or morereference exchange servers may refer to a reference exchange serverassociated with a reference exchange in which traders trade one or moretradable items affecting the price of a target tradable item. Eachreference exchange server of the one or more reference exchange serversmay provide market data of the associated reference exchange. The one ormore reference exchange servers 120 may include the target exchangeserver 130. Each of the one or more news providing servers may providenews affecting the price of the target tradable item. Each of the one ormore social network service (SNS) servers may provide SNS informationaffecting the price of the target tradable item. For example, the SNSinformation may include contents of article affecting the price of thetarget tradable item, the number of views of the article, and the numberof followers following the author of the article. Each of the one ormore online shopping service servers may provide shopping informationaffecting the price of the target tradable item. For example, theshopping information may include the price change, supply and demand ofan item affecting the price of the target tradable item.

The target exchange server 130 may refer to an exchange serverassociated with a target exchange in which the target tradable item istraded.

The one or more market reference information providing servers 140 mayprovide market reference information. The one or more market referenceinformation providing servers 140 may include.

According to an embodiment, the high frequency trading apparatus 110 maypredict the market of the target exchange at a future time (e.g., at anear future time after a predetermined time) based on market-relatedinformation such as market data of one or more exchanges, newsinformation, SNS information, and shopping information. And then, thehigh frequency trading apparatus 110 may generate an order signal for atarget tradable item based on the market prediction and transmit it tothe target exchange server 130. For high frequency trading, it is veryimportant to generate and transmit orders at a high speed based on themarket prediction. For this reason, in high frequency trading, evenmicrosecond latency must be considered, and the high frequency tradingapparatus 110 may be collocated close to the target exchange server 130in order to reduce the latency.

According to an embodiment, the high frequency trading apparatus 110 mayreceive market data of the reference exchanges from the one or morereference exchange servers 120. In this example, the market data mayinclude data on tradable items traded in the reference exchangesassociated with the one or more reference exchange server 120. Forexample, the market data may include an order book of (at least some of)tradable items traded in the one or more reference exchange servers 120.In an embodiment, the market data may include data on a target tradableitem. For example, the market data may include the top of an order bookfor the target tradable item, a list of (valid) orders for the targettradable item, a response of the target exchange server 130 to aprevious order for the target tradable item, etc. In another embodiment,if the high frequency trading apparatus 110 is collocated far from amarket-related information server, the data of the market-relatedinformation server 120 may be excluded from the market-relatedinformation in consideration of the relatively long latency beforereceiving the data of the market-related information server.

According to an embodiment, the high frequency trading apparatus 110 mayanalyze market-related information to generate an order. For example,the high frequency trading apparatus 110 may analyze market-relatedinformation (or market prediction reference data generated based on themarket-related information), to predict market (e.g., the price of atarget item) at a future time point (e.g., 1 second later), and generatean order based on the market prediction.

According to an embodiment, the process of analyzing the market-relatedinformation and/or market prediction reference data generated based onthe market-related information may be performed by a machine learningmodel. By using a machine learning model, it is possible to findfeatures that classical algorithms cannot find from complex marketconditions, and also predict future market and use the future marketprediction to generate orders to gain an advantage in trading.Meanwhile, in high frequency trading, it is very important to analyzemarket-related information quickly and generate orders, but since ageneral processor does not have a storage space and operationalresources for processing the computation of a complex machine learningmodel, there is a problem in that speed and efficiency are very limitedif the machine learning model is driven using the general processor.Accordingly, the high frequency trading apparatus 110 according to anembodiment of the present disclosure may include a dedicated accelerator(e.g., a neural processing unit (NPU)) for a machine learning model, andthe dedicated accelerator may be implemented as the ASIC.

Meanwhile, for using a machine learning model, appropriatepre/post-processing may be required. For example, a process ofgenerating market prediction reference data for the machine learningmodel from market-related information or generating orders based onfuture market prediction data received from the machine learning modelmay be required. Such pre/post-processing may be continuously changedaccording to changes in market conditions, regulations, compensationrules for market makers, etc. Implementing such pre/post-processingunits as the ASIC customized for a specific purpose may cause a problemin that changes in the pre/post processing requires re-manufacturing ofa processor for performing the changed pre/post processing. Accordingly,processes excluding driving of the machine learning model may beperformed by a reprogrammable, reconfigurable and/or design-changeableprocessor such as FPGA.

As described above, the processor running the machine learning model maybe configured with a dedicated accelerator (e.g., NPU ASIC) predesignedor preconfigured to process the operations of the machine learning modelquickly and efficiently. The pre/post-processing can be flexibly changedin accordance with the changed market conditions by using a processorcapable of reprogramming and/or re-designing (e.g., FPGA). In this way,by using two or more different processors, it is possible tosimultaneously achieve both the implementation of flexiblepre/post-processing and efficient and fast arithmetic processing ofmachine learning models. The internal components of the high frequencytrading apparatus 110 will be described in detail below with referenceto FIGS. 2 to 4 .

FIG. 2 is a block diagram illustrating internal components of the highfrequency trading apparatus 110 according to an embodiment of thepresent disclosure.

The high frequency trading apparatus 110 may include a memory 210, aprocessor 220, a communication module 230, and an input and outputinterface 240. As illustrated in FIG. 2 , the high frequency tradingapparatus 110 may be configured to communicate information and/or datathrough a network by using the communication module 230.

The memory 210 may include any non-transitory computer-readablerecording medium. According to an embodiment, the memory 210 may includea permanent mass storage device such as random access memory (RAM), readonly memory (ROM), disk drive, solid state drive (SSD), flash memory,and so on. As another example, a non-destructive mass storage devicesuch as ROM, SSD, flash memory, disk drive, etc. may be included in thehigh frequency trading apparatus 110 as a separate permanent storagedevice that is separate from the memory. In addition, an operatingsystem and at least one program code (e.g., code for arithmeticprocessing, pre/post processing, and order transmission of a machinelearning model installed and driven in the high frequency tradingapparatus 110) may be stored in the memory 210. In FIG. 2 , the memory210 is illustrated as a single memory for convenience, but the processor220 may include a plurality of memories.

These software components may be loaded from a computer-readablerecording medium separate from the memory 210. Such a separatecomputer-readable recording medium may include a recording mediumdirectly connectable to the high frequency trading apparatus 110, andmay include a computer-readable recording medium such as a floppy drive,a disk, a tape, a DVD/CD-ROM drive, a memory card, etc., for example. Inanother example, the software components may be loaded into the memory210 through the communication module 230 rather than thecomputer-readable recording medium. For example, at least one programmay be loaded into the memory 210 based on a computer program (e.g., aprogram or the like for analyzing market-related information, predictingfuture markets, generating and transmitting orders, etc.) installed bythe files provided by the developers, or by a file distribution systemthat distributes an installation file of an application through acommunication module 230.

The processor 220 may be configured to process the commands of thecomputer program by performing basic arithmetic, logic, and input andoutput operations. The commands may be provided to a user terminal (notillustrated) or another external system by the memory 210 or thecommunication module 230. For example, the processor 220 may generatefuture market prediction data based on the market prediction referencedata using the machine learning model, and may generate orders based onthe future market prediction data. The generated orders may betransmitted to the target exchange server 130.

The communication module 230 may provide a configuration or function forthe user terminal (not illustrated) and the high frequency tradingapparatus 110 to communicate with each other through a network, and mayprovide a configuration or function for the high frequency tradingapparatus 110 to communicate with an external system (e.g., a separatecloud system). For example, control signals, commands, data, etc.provided under the control of the processor 220 of the high frequencytrading apparatus 110 may be transmitted to the user terminal and/or theexternal system through the communication module 230 and the networkthrough the communication module of the user terminal and/or an externalsystem. For example, the external system such as target exchange server130 may receive the orders etc. from the high frequency tradingapparatus 110.

In addition, the input and output interface 240 of the high frequencytrading apparatus 110 may interface with a device (not illustrated) forinput or output that may be connected to the high frequency tradingapparatus 110 or may be included in the high frequency trading apparatus110. For example, the input and output interface 240 may include atleast one of a PCI express interface or an Ethernet interface. In FIG. 2, the input and output interface 240 is illustrated as a componentconfigured separately from the processor 220, but embodiments are notlimited thereto, and the input and output interface 240 may beconfigured to be included in the processor 220. The high frequencytrading apparatus 110 may include more components than those illustratedin FIG. 2 .

The processor 220 of the high frequency trading apparatus 110 may beconfigured to manage, process, and/or store the information and/or datareceived from a plurality of user terminals and/or a plurality ofexternal systems such as one or more market-related information servers120 and the target exchange server 130. According to an embodiment, theprocessor 220 may receive market-related information from the one ormore market-related information servers 120. The processor may predictfuture market based on the received market-related information andgenerate orders based on the future market prediction. In FIG. 2 , theprocessor 220 is illustrated as a single processor for convenience, butthe processor 220 may include a plurality of processors. For example,the processor 220 may include at least one processor implemented in anFPGA for pre-processing and post-processing, and a dedicated acceleratorimplemented in an ASIC for a machine learning model, in which the atleast one processor implemented in the FPGA may execute one or moreinstructions stored in a first memory, and the dedicated acceleratorimplemented in the ASIC may execute one or more instructions stored in asecond memory.

FIG. 3 is a diagram illustrating internal components of the processoraccording to an embodiment.

According to an embodiment, the processor 300 (e.g., a processor in thehigh frequency trading apparatus 110 or high frequency order generatingapparatus) may include FPGA 302 for pre/post processing and a dedicatedaccelerator 340 (e.g., a dedicated accelerator implemented as ASICs) forthe machine learning model. The FPGA 302 for pre/post processing mayinclude an input handler 310, an input generation unit 330, an ordergeneration unit 350, and an output handler 360. Although the internalcomponents of the processor are illustrated separately by function inFIG. 3 , this does not necessarily mean that they are physicallyseparated. In addition, the internal components of the processorillustrated in FIG. 3 are only an example, and it is not intended todepict essential configurations only. Accordingly, in some embodiments,the processor may be implemented differently, such as by additionallyincluding components other than those internal components illustrated,or by omitting some of the illustrated components.

According to an embodiment, the processor may receive the market-relatedinformation from the one or more market-related information servers 120.The received market-related information may include data on items tradedin the one or more exchanges associated with the one or more exchangeservers. For example, the market-related information may include anorder book of (at least some of) items traded in an exchange server, andadditionally, the market-related information may include data on atarget item. For example, the market-related information may include atop of an order book for the target item, a list of (valid) orders forthe target item, the response to a previous order for the target item inthe target exchange associated with the target exchange server 130, etc.The processor may receive the market-related information data from theone or more market-related information servers 120 every time themarket-related information data needs to be updated, or may receivemarket-related information data periodically (e.g., every 0.1 seconds)from the one or more market-related information servers 120. Since it isimportant to process data at a high speed in high frequency trading, inan embodiment, the market-related information data may be receivedthrough a User Datagram Protocol (UDP) having a high data transmissionrate. However, in some embodiments, other communication protocols (e.g.,TCP/IP) may be used to receive market-related information data as needed(e.g., to ensure reliability of data).

The input handler 310 may parse and/or decode the receivedmarket-related information data. According to an embodiment, themarket-related information data may be received in a plurality of datapackets and may be received through a plurality of lines. If themarket-related information data is received through a plurality oflines, each input handler 310 may parse and/or decode the market-relatedinformation data received through the plurality of lines in differentways according to the data format or standard. The market-relatedinformation data parsed/decoded through the input handler 310 may beprovided to the input generation unit 330 to generate market predictionreference data as input data of the machine learning model.

The input generation unit 330 may generate the market predictionreference data based on at least a portion of the market-relatedinformation. According to an embodiment, the input generation unit 330may select one or more reference features of one or more items fromamong the market-related information to form market prediction referencedata. For example, the input generation unit 330 may include a featureextraction unit for extracting or selecting reference features includedin the market prediction reference data.

In an embodiment, one or more items included in the market predictionreference data may include items that may be a leading indicator of avariation in market conditions of the target item. For example, if thetarget item to be ordered is the stock (spot) of Company A, data onfutures stocks related to company A's stock, option stocks related tocompany A's stock, stocks related to company A included in otherexchanges, futures stocks for products (e.g., crude oil, etc.)associated with company A, etc. may be included in the market predictionreference data. In addition, in an embodiment, the one or more referencefeatures included in the market prediction reference data may includeinformation meaningful in predicting market conditions of the targetitem. For example, the reference features may include variousinformation extractable from the order book of one or more items, suchas a market price (transaction price), a price and quantity at the topof the order book of a buying side, a price and quantity at the top ofthe order book of a selling side, the number of sellers wishing to sell,the ask price for buy of the next stage at the top of the order book,the ask price for sell of the next stage at the top of the order book,the variance of the ask price included in the order book, etc.,information obtained by processing the information and/or reliability ofthe information, etc. The configuration of the market predictionreference data will be described below in more detail with reference toFIG. 8 .

The market prediction reference data generated by the input generationunit 330 may be transmitted to the dedicated accelerator 340 as inputdata for the machine learning model and may be fed to the machinelearning model. According to an embodiment, the dedicated accelerator340 may be a neural processing unit (NPU) specialized for arithmeticprocessing of a machine learning model and may be implemented as anapplication-specific semiconductor (ASIC) specialized for driving amachine learning model. The dedicated accelerator 340 may use themachine learning model to derive future market prediction dataassociated with an order for a target item based on the marketprediction reference data. For example, the dedicated accelerator 340may receive the market prediction reference data as the input data tothe machine learning model, and derive future market prediction datathat predicts a price (e.g., a market price or a median price) of thetarget item at a specific time in the future. According to anembodiment, the specific time in the future may be a time point obtainedby the current time point plus a latency in ordering a target item tothe target exchange server 130. That is, it is possible to predict theprice of the target item near the time point when the order is expectedto arrive at the target exchange server 130 in consideration of thelatency. The machine learning model for deriving future marketprediction data associated with the order for the target item will bedescribed in detail below with reference to FIGS. 7 and 11 .

According to an embodiment, instead of directly providing the marketprediction reference data generated by the input generation unit 330 tothe dedicated accelerator 340 for the machine learning model, theprocessor (e.g., at least one processor for pre/post processing) mayfirst determine whether to use the machine learning model and thentransmit the market prediction reference data to the dedicatedaccelerator 340 for the machine learning model only if it is determinedto use the machine learning model. For example, the processor maydetermine a prediction complexity based on the market predictionreference data generated by the input generation unit 330, and accordingto the determined prediction complexity, determine whether to generatethe orders (or future market prediction data associated with orders) byusing the machine learning model or to generate orders by using apredetermined rule. In this case, the prediction complexity may includetwo or more complexity classes (e.g., low, moderate, high, etc.)according to the complexity. The internal components of the processorincluding the complexity determining unit, and the process ofdetermining the prediction complexity by the complexity determining unitand processing the market prediction reference data accordingly, will bedescribed in detail below with reference to FIGS. 4 and 5 .

The order generation unit 350 may receive the future market predictiondata from the machine learning model, and generate orders in the targetexchange server 130 based on the future market prediction data. Forexample, the order generation unit 350 may generate orders for thetarget item according to a predetermined rule based on the predictedprice of the target item at time point of the future, which is inferredfrom the machine learning model. As a specific example, if the price ofthe target item is predicted to increase, the order generation unit 350may immediately generate a new request order to buy a target item orcorrect the ask price of an existing request order to sell a targetitem. According to an embodiment, each order may include information onthe type of order (new order, order cancellation, order correction),whether to buy or sell, price (ask price), quantity, etc. for the targetitem.

Additionally, the orders generated by the order generation unit 350 maybe transmitted to the output handler 360. According to an embodiment,the output handler 360 may check a risk based on the generated orders,or determine whether or not a regulation on market making is satisfied.Additionally or alternatively, the output handler 360 may performappropriate processing on the previously generated orders according tothe format, standard, and protocol of the orders required by the targetexchange server 130.

The orders generated by the order generation unit 350 (or post-processedby the output handler 360) may be transmitted to the target exchangeserver 130. According to an embodiment, the processor (e.g., at leastone processor for pre/post processing) may receive a market response tothe transmitted orders in the target exchange associated with the targetexchange server 130. In this case, the processor may update the orderdetails for the target exchange server 130 based on the received marketresponse, and the order details for the target exchange server 130 maybe used as market-related information to create a next order, or may beused as basic data for the order generating unit 350 to create an order.

FIG. 4 is a diagram illustrating internal components of the processorincluding the complexity determining unit 400 that determines predictioncomplexity according to an embodiment of the present disclosure.

The processor may further include a host device 440 for driving thecomplexity determining unit 400 and a trading engine 430 in addition tothe components described above with respect to FIG. 3 . In FIG. 4 , thecomponents newly added to the internal components of the processordescribed above with respect to FIG. 3 will be mainly described.

According to an embodiment, instead of directly providing the marketprediction reference data generated by the input generation unit 330 toa dedicated accelerator 420 as input data for the machine learningmodel, the FPGA 302 may first determine whether to use the machinelearning model and then provide the market prediction reference data tothe dedicated accelerator 420 for the machine learning model only if itis determined to use the machine learning model. For example, thecomplexity determining unit 400 included in the processor may determinea prediction complexity based on market prediction reference data. Andthen, the complexity determining unit 400, according to the determinedprediction complexity, may determine whether to generate the orders (orfuture market prediction data associated with the orders) by using atrading logic 410, or whether to generate the orders (or future marketprediction data associated with the orders) using the machine learningmodel, or whether to generate the orders (or future market predictiondata associated with the orders) by using the trading engine 430included in the host device 440. For example, the complexity determiningunit 400 may determine the prediction complexity based on the currentmarket price, the order quantity for each order price, the number ofcounterparties for trading of the one or more items, etc. of one or moreitems included in the market prediction reference data. As anotherexample, the complexity determining unit 400 may determine a datapattern of one or more reference features included in the marketprediction reference data, determine whether or not the data patternsatisfies a predetermined condition, and then determine the predictioncomplexity according to the determination result.

In an embodiment, if the complexity determination unit 400 determines togenerate the orders (or future market prediction data associated withthe orders) by using the trading logic 410, the order generation unit350 may generate the orders according to the trading logic 410 (e.g.,predefined rules).

In another embodiment, if the complexity determination unit 400determines to generate the future market prediction data associated withthe orders by using the machine learning model, the market predictionreference data may be provided to the dedicated accelerator 420 as inputdata for machine learning, in which the dedicated accelerator 420 mayprocess operations of at least a portion of the machine learning model,thereby generating future market prediction data associated with theorders from the market prediction reference data. The future marketprediction data generated as described above may be provided to theorder generation unit 350 of the processor, and the orders may begenerated based on the future market prediction data.

In another embodiment, if the complexity determination unit 400determines to generate the orders (or future market prediction dataassociated with the orders) by using the trading engine 430 included inthe host device 440, the market prediction reference data may beprovided to the host device 440, and the trading engine 430 included inthe host device 440 may generate the orders by using a logic based onthe predetermined rules, or derive future market prediction dataassociated with the orders based on the market prediction reference databy using a relatively light machine learning model. The future marketprediction data associated with the orders derived by the trading engine430 may be provided to the order generation unit 350 of the processor.In an embodiment, if the trading engine 430 generates the orders using alogic based on a predefined rule, the orders may be directly fed to theoutput handler 360 of the processor without going through the ordergenerating unit 350 of the processor.

A method of determining the prediction complexity based on the marketprediction reference data and processing the market prediction referencedata accordingly will be described in more detail below with referenceto FIG. 5 .

FIG. 5 is a diagram illustrating an example of a method of determining aprediction complexity 520 and determining whether or not to use amachine learning model 552 according to the determined predictioncomplexity, according to an embodiment of the present disclosure.

The FPGA 302 of the high frequency trading apparatus 110 or highfrequency orders generating apparatus may generate market predictionreference data 510 (e.g., reference feature map) based on themarket-related information, and determine the prediction complexity 520based on the market prediction reference data 510. According to anembodiment, the prediction complexity may reflect at least one of acomplexity of the market prediction reference data or an operationalcomplexity for inferring data associated with an order based on themarket prediction reference data. In addition, in an embodiment, theprediction complexity may be classified into two or more complexityclasses. For example, the prediction complexity may be classified intothree complexity classes of low, moderate, and high, as illustrated.

According to an embodiment, the processor may determine the predictioncomplexity 520 based on the current market price, the order quantity foreach order price, the number of counterparties for trading of the one ormore items, etc. of one or more items included in the market predictionreference data 510. According to another embodiment, the processor maydetermine a data pattern of one or more reference features included inthe market prediction reference data 510, determine whether or not thedata pattern satisfies a predetermined condition, and then determine theprediction complexity 520 according to the determination result.

For example, for the reference item having a very high statisticalcorrelation with the target item, if a variation of the moving averageof the median price (e.g., a weighted average of ToB prices of thebuying side and ToB prices of the selling side) is equal to or greaterthan a first predetermined threshold, or equal to or less than a secondpredetermined threshold, the processor may classify the predictioncomplexity into a low class. Specifically, by comparing the movingaverage of the median price of the reference item in the interval

$T = \left\lbrack {t,\ {t - \frac{M}{2}}} \right\rbrack$

with the moving average of the median price of the reference item in theinterval

${T = \left\lbrack {{t - \frac{M}{2} + 1},\ {t - M + 1}} \right\rbrack},$

if the former is τ₁(>1) times the latter or greater, it may bedetermined that the price of the target item is predicted to increasewith high probability, and thus the prediction complexity may bedetermined into the low class. Alternatively, if the former is τ₂(<1)times the latter or less, it may be determined that the price of thetarget item is predicted to decrease with high probability, and thus theprediction complexity may be determined into the low class. If theformer is neither τ₁(>1) times the latter or greater, nor τ₂(<1) timesthe latter or less, the prediction complexity may be determined into amoderate class or high class.

As another example, for all reference items included in the marketprediction reference data 510, if the variance of the median price for aspecific time period is equal to or less than a third predeterminedthreshold, since the possibility of price variation is low, it may bedetermined that a profit according to the spread can be achieved, andthe prediction complexity may be determined into the low class.

According to an embodiment, the processor may first determine theprediction complexity 520 based on the market prediction reference data510 and then determine whether or not to generate the orders (or futuremarket prediction data associated with the order) by using the machinelearning model according to the determined prediction complexity. In anembodiment, if the prediction complexity is determined into the lowclass, FPGA 302 may generate the orders based on the market predictionreference data according to a rule-based logic 532. For example, for areference item having a statistically very high correlation with thetarget item, if the variation of the moving average of the median priceis equal to or greater than the first predetermined threshold, it may bedetermined that the price of the target item will increase, and an orderto buy the target item may be generated. If the variation of the movingaverage of the median price is equal to or less than the secondpredetermined threshold, it may be determined that the price of thetarget item will decrease, and an order to sell the target item may begenerated. In another example in which the FPGA 302 generates the ordersaccording to the rule-based logic 532, for all reference items includedin the market prediction reference data 510, if a variance of the medianprice for a specific time period is equal to or less than a thirdpredetermined threshold, an order to buy and an order to sell may begenerated in both ToBs to achieve a profit according to the spread. Inthis case, as there occur deviations from the criteria described above(if variance is increased) over time, an order to cancel the existingorder may be generated in order to minimize the risk caused by pricevariation.

In another embodiment, if the prediction complexity is determined intothe high class, the FPGA 302 may provide the market prediction referencedata 510 to a dedicated accelerator 550 (e.g., a dedicated acceleratorimplemented in the ASIC) for the machine learning model 552, in whichthe dedicated accelerator 550 may generate future market prediction dataassociated with the order for the target item based on the marketprediction reference data 510 by using the machine learning model 552.For example, The dedicated accelerator 550 may process at least someoperations of the machine learning model 552 to generate the futuremarket prediction data that predicts the price of a target item (marketprice or median price) at a specific point in the future based on themarket prediction reference data 510.

In another embodiment, if the prediction complexity is determined intothe moderate class, the FPGA 302 may provide the market predictionreference data 510 to the host device 440. The host device 440 may use arule-based logic or light machine learning model 542 with the marketprediction reference data 510 to generate the orders or future marketprediction data associated with the order.

As described above, by determining the prediction complexity based onthe market prediction reference data and generating future marketprediction data by using the machine learning model if the predictioncomplexity is high, it is possible to find the features that classicalalgorithms cannot find and also to predict exact future market.Moreover, by determining the prediction complexity based on the marketprediction reference data and generating the orders directly by usingrelatively simple trading logic if the prediction complexity is low, itis possible to quickly transmit orders and gain an edge in trading.

FIG. 6 is a diagram illustrating an example of a method for generatinghigh frequency orders according to an embodiment of the presentdisclosure. According to an embodiment, the processor (e.g., at leastone processor included in the apparatus for generating high frequencyorders) may generate market prediction reference data 620 based onmarket-related information 610, and generate future market predictiondata associated with an order based on the market prediction referencedata 620 by using a machine learning model 630. The future marketprediction data generated as described above may be fed to an ordergeneration unit 640. And then the order generation unit 640 may generateorders for a target item to the target exchange server 130.

According to an embodiment, each order signal generated by the ordergeneration unit 640 may include information on the type of order (e.g.,new orders, cancellation orders, correction orders, etc.), whether tobuy or sell, a price, an order quantity, etc. Here, the order signal mayinclude one or more orders. For example, as illustrated, the ordergeneration unit 640 may generate order signal 650 including three ordersof “new/sell/$102.0/20 qty”, “cancel/sell/$100.5/10 qty”, and“cancel/buy/$98.0/30 qty” (indicating, in order, order type/buy orsell/order price/order quantity) based on the future market predictiondata received from the machine learning model 630.

The generated order signal 650 may be transmitted to the target exchangeserver 130, and the processor may receive a market response to thetransmitted order in the target exchange associated with the targetexchange server 130. According to this market response, the processormay update the order details for the target item. In this case, theorder details may refer to a list of currently valid orders (canceled orunconcluded orders) among the orders transmitted by the apparatus forgenerating high frequency orders. For example, after the order signal650 illustrated in FIG. 6 is transmitted to the target exchange server130, if a market response is received from the target exchange server130 indicating that all three orders included in the order signal 650have been normally received, the order details for the target item maybe updated by reflecting the order signal 650. Specifically, if orderdetails 660 before transmitting the order signal 650 include“sell/$101.0/10 qty”, “sell/$100.5/10 qty”, “buy/$99.5/20 qty”, and“buy/$98.0/30 qty”, updated order details 670 may be changed to“sell/$102.0/20 qty”, “sell/$101.0/10 qty”, and “buy/$99.5/20 qty”.Then, the updated order details 670 may be included in the data (e.g.,market-related information or data considered by the order generationunit, etc.), which is based on for generating the order signal by theapparatus for generating the high frequency orders. The order detailsmay also be referred to as an open order or an order map.

FIG. 7 is a diagram illustrating an example in which a machine learningmodel 700 outputs future market prediction data 720 based on marketprediction reference data 710 according to an embodiment of the presentdisclosure. According to an embodiment, the machine learning model 700may output the future market prediction data 720 associated with anorder of a target item based on the market prediction reference data710. For example, the machine learning model may output a predictedprice (e.g., a market price or a median price, etc.) of a target item ata specific point in the future based on the market prediction referencedata 710. According to an embodiment, the market prediction referencedata 710 fed to the machine learning model 700 may include a referencefeature map including one or more reference features for one or moreitems at one or more time points. The market prediction reference data710 corresponding to input data of the machine learning model 700 willbe described in detail below with reference to FIG. 8 .

According to an embodiment, the machine learning model 700 may betrained to infer future market prediction data associated with orders ina target exchange server 130 by using market prediction reference datagenerated based on market-related information data from one or moremarket-related information servers 120. For example, the machinelearning model 700 may be trained by supervised learning to infer themedian price of the target item at the next time point based on marketprediction reference data in a time interval including a total of Mconsecutive time points, by using market prediction reference data fromtime point (t) to time point (t+M−1) generated based on reference marketdata of one or more exchanges associated with the one or more exchangeservers 120 and reference market data of the target exchange associatedwith the target exchange server 130, and median price data of the targetitem at time point (t+1).

The future market prediction data 720 output by the machine learningmodel 700 may include information associated with orders in the targetexchange server, and a processor (e.g., at least one processor of a highfrequency trading apparatus 110 or apparatus for generating highfrequency orders) may generate orders for the target item based on apredetermined rule based on the future market prediction data 720.

According to an embodiment, the machine learning model 700 of thepresent disclosure may be an artificial neural network model. Theartificial neural network model will be described below in detail withreference to FIG. 11 .

FIG. 8 is a diagram illustrating an example of a configuration of marketprediction reference data 810 for the machine learning model accordingto an embodiment of the present disclosure. The high frequency tradingapparatus 110 (e.g., at least one processor included in the highfrequency trading apparatus 110) may generate the market predictionreference data 810 based on market-related information received from oneor more market-related information servers 120. According to anembodiment, the market prediction reference data 810 may include areference feature map including one or more reference features for oneor more items at one or more time points.

For example, the reference feature map may include N reference featuresfor K reference items at M time points, as illustrated in FIG. 8 . Inthe illustrated example, data 820 at a specific time point (time (m) inFIG. 8 ) in a reference feature map included in the market predictionreference data may include one or more reference features (the price andquantity at the top of the order book on the buying side, the price andquantity at the top of the order book on the selling side in FIG. 8 )for one or more reference items (first reference item, second referenceitem, and third reference item in FIG. 8 ) at a specific time point. Inaddition, data 830 for a specific reference feature (n-th referencefeature in FIG. 8 ) in the reference feature map included in the marketprediction reference data may include specific reference features forone or more reference items at one or more time points (from time point(t−M+1) to time point (t) in FIG. 8 ). In an embodiment, the referencefeature map may be generated such that one or more reference featuresfor different reference items intersect each other.

According to an embodiment, the one or more reference items included inthe market prediction reference data 810 may be items serving as aleading indicator of the market conditions of the target item to beordered. For example, if the target item to be ordered is the companyA's stocks (spot), futures stocks related to the company A's stock,option stocks related to the company A's stock, stocks related company Aincluded in another exchange, and futures stocks for products related tocompany A, etc. may be included in the one or more reference items. Inan embodiment, the one or more reference items may include a targetitem. That is, the high frequency trading apparatus 110 may predict thefuture market conditions of the target item based on the marketprediction reference data including the data on the target item. Inaddition, in an embodiment, the information on each reference item maybe included as a code (symbol) associated with each reference item.

According to an embodiment, one or more reference features included inthe market prediction reference data 810 may include informationmeaningful in predicting market conditions of a target item. Forexample, the reference features may include various informationextractable from the order book of one or more reference items, such asa market price (transaction price), a price and quantity at the top ofthe order book of a buying side, a price and quantity at the top of theorder book of a selling side, the number of sellers wishing to sell, theask price for buy of the next stage at the top of the order book, theask price for sell of the next stage at the top of the order book, thevariance of the ask price included in the order book, etc., informationobtained by processing the information and/or reliability of theinformation, etc. In an embodiment, these one or more reference featuresmay be extracted from each of the one or more reference items.

The market prediction reference data 810 configured as described abovemay be transmitted to a dedicated accelerator for the machine learningmodel by a processor (e.g., at least one processor of a high frequencytrading apparatus 110), and may be input to the machine learning model.Additionally or alternatively, the processor may determine theprediction complexity based on the market prediction reference data 810to determine whether to use the machine learning model or to use a hostdevice 440, or to generate orders based on rules within thecorresponding processor. According to this determination, the marketprediction reference data 180 may be transmitted to the dedicatedaccelerator or the host device 440 for a machine learning model, or maybe used to derive future market prediction data associated with theorder for the target item based on the rules within the correspondingprocessor.

FIG. 9 is a flowchart illustrating an example of a method 900 ofoperating the high frequency trading apparatus 110 including the FPGA302 and the NPU 340 according to an embodiment of the presentdisclosure. According to an embodiment, at S910, the method 900 may beinitiated by the FPGA 302 receiving first market data of a referenceexchange associated with a reference exchange server and second marketdata of a target exchange associated with the target exchange server130. Each market data may include transaction information, etc. on itemstraded in each exchange. For example, the first market data may includeinformation on an order book of items traded in the reference exchangesassociated with the one or more reference exchange servers, and thesecond market data may include at least a portion of market responses inthe target exchange associated with the target exchange server 130 andinformation on the order book of one or more items (e.g., information onthe ToB of the target item).

Then, at S920, the FPGA 302 may generate market prediction reference asinput data of the machine learning model based on at least one of thefirst market data or the second market data. According to an embodiment,the market prediction reference data may include one or more referencefeatures for one or more items at one or more time points, in which theinformation on the one or more items may be included as a code (symbol)for each item. In addition, the one or more items included in the marketprediction reference data may include items indicating a leadingindicator of a variation in market conditions of the target item andtarget items, and target items, which are targets of orders in thetarget exchange server 130.

According to an embodiment, before generating the market predictionreference data from the market data, parsing and decoding may beperformed. For example, after parsing and decoding of at least one ofthe first market data or the second market data, the FPGA 302 maygenerate market prediction reference data as the input data of themachine learning model based on the result.

Then, at S930, the NPU 340 may process at least some operations for themachine learning model. For example, the NPU 340 may receive the marketprediction reference data generated from the FPGA 302, feed the machinelearning model with the market prediction reference data so that themachine learning model may perform a series of operations on the marketprediction reference data to derive future market prediction data.According to an embodiment, the machine learning model may be any modelconfigured to infer future market prediction data associated with ordersin the target exchange server by using market prediction reference datagenerated based on reference market data of one or more exchanges.

According to an embodiment, instead of providing the generated marketprediction reference data directly to the NPU 340, the FPGA 302 maydetermine a prediction complexity based on the market predictionreference data, and based on the determined prediction complexity,determine whether to generate the orders based on a predetermined ruleor to use the machine learning model. In an embodiment, if it isdetermined to generate the orders according to the predetermined rule,the FPGA 302 may generate the orders according to the predetermined rulebased on the market prediction reference data. In another embodiment, ifit is determined to generate orders by using the machine learning model,the NPU 340 may process at least some operations for the machinelearning model, and provide future market prediction data output fromthe machine learning model to the FPGA 302.

Additionally, the high frequency trading apparatus 110 may furtherinclude a host device 440 configured to drive the trading engine, andthe prediction complexity may include three complexity classes accordingto the complexity. For example, the prediction complexity may includethree complexity classes of high, moderate, and low according to thecomplexity. In this case, the market prediction reference data may beprovided to at least one of the FPGA 302, the host device 440, or theNPU 340 according to three complexity classes to generate orders in thetarget exchange server 130.

Then, at S940, the NPU 340 may provide the future market prediction dataoutput from the machine learning model to the FPGA 302. At S950, theFPGA 302 may generate orders in the target exchange server 130 based onthe future market prediction data received from the machine learningmodel. Additionally, the FPGA 302 may process the generated ordersaccording to a protocol required by the target exchange server 130. Theorders generated (processed according to a protocol required by thetarget exchange server 130) as described above may be transmitted to thetarget exchange server 130.

FIG. 10 is a flowchart illustrating an example of a method 1000 forgenerating high frequency orders according to an embodiment of thepresent disclosure. According to an embodiment, at S1010, the method1000 may be initiated by a processor (e.g., at least one processor of anapparatus for generating high frequency orders) generating marketprediction reference data based on market-related information datacollected from one or more market-related information servers 120. In anembodiment, the market prediction reference data may include a referencefeature map including one or more reference features for one or moreitems at one or more time points, and the one or more reference featuresmay be extracted from each of the one or more reference items. Accordingto an embodiment, the one or more reference items included in the marketprediction reference data may include a target item.

Then, at S1020, the processor may generate future market prediction dataassociated with the order of the target exchange server from the machinelearning model based on the generated market prediction reference data.In this example, the machine learning model may be any machine learningmodel configured to infer future market prediction data associated withan order in the target exchange by using market prediction referencedata generated based on market-related information. According to anembodiment, the future market prediction data received from the machinelearning model may include data associated with the price prediction ofthe target item at a specific time point, in which the specific timepoint may be a time point obtained by the current time point plus alatency in ordering a target item to the target exchange server 130.

Alternatively, the processor may first determine the predictioncomplexity based on the reference feature map and then determine whetheror not to apply the market prediction reference data to the machinelearning model based on the determined prediction complexity, andprovide the market prediction reference data to the machine learningmodel only if it is determined to apply the market prediction referencedata to the machine learning model. In this case, the predictioncomplexity may be determined in various ways. For example, the processormay determine the prediction complexity based on the informationincluded in the reference feature map such as the current price of oneor more items, the number of items per order price, and the number ofcounterparties of the one or more items. As another example, theprocessor may determine a data pattern of one or more reference featuresof the information included in the reference feature map, determinewhether or not the determined data pattern satisfies a predeterminedcondition, and then determine the prediction complexity according to thedetermination result.

Then, at S1030, the processor may generate the orders for the targetitem of the target exchange server based on the future market predictiondata. According to an embodiment, each order may include information onthe type of order (e.g., new orders, cancellation orders, or correctionorders), whether to buy or sell, a price, and a quantity for the targetitem.

According to an embodiment, the generated orders may be transmitted tothe target exchange server 130, and a response according to thetransmission of the orders may be received from the target exchangeserver. In this case, the target exchange order details may be updatedbased on the response to the orders transmission, and the updated targetexchange order details may be included in the market-related informationand used to generate another order.

FIG. 11 illustrates an example of an artificial neural network model1100 according to an embodiment of the present disclosure. In machinelearning technology and cognitive science, the artificial neural networkmodel 1100 as an example of the machine learning model refers to astatistical learning algorithm implemented based on a structure of abiological neural network, or to a structure that executes suchalgorithm.

According to an embodiment, the artificial neural network model 1100 mayrepresent a machine learning model that acquires a problem solvingability by repeatedly adjusting the weights of synapses by the nodesthat are artificial neurons forming the network through synapticcombinations as in the biological neural networks, thus training toreduce errors between a target output corresponding to a specific inputand a deduced output. For example, the artificial neural network model1100 may include any probability model, neural network model, etc., thatis used in artificial intelligence learning methods such as machinelearning and deep learning.

According to an embodiment, the neural network model 1100 may include anartificial neural network model configured to infer data associated withan order in a target exchange server using market prediction referencedata generated based on the market-related information from the one ormore market-related information servers 120.

The artificial neural network model 1100 is implemented as a multilayerperceptron (MLP) formed of multiple nodes and connections between them.The artificial neural network model 1100 according to an embodiment maybe implemented using one of various artificial neural network modelstructures including the MLP. As illustrated in FIG. 11 , the artificialneural network model 1100 includes an input layer 1120 to receive aninput signal or data 1110 from the outside, an output layer 1140 tooutput an output signal or data 1150 corresponding to the marketprediction reference data, and (n) number of hidden layers 1130_1 to1130_n (where n is a positive integer) positioned between the inputlayer 1120 and the output layer 1140 to receive a signal from the inputlayer 1120, extract the features, and transmit the features to theoutput layer 1140. In an example, the output layer 1140 receives signalsfrom the hidden layers 1130_1 to 1130_n and outputs them to the outside.

The method of training the artificial neural network model 1100 includesthe supervised learning that trains to optimize for solving a problemwith inputs of teacher signals (correct answers), and the unsupervisedlearning that does not require a teacher signal. In an embodiment, theneural network model 1100 may be trained by the supervised and/orunsupervised learning to infer the data associated with the orders inthe target exchange server. For example, the artificial neural networkmodel 1100 may be trained by the supervised learning to infer thereference price of the target item at a specific time from the marketprediction reference data.

The artificial neural network model 1100 trained as described above maybe stored in the memory of a high frequency trading apparatus 110 or amemory (not illustrated) of the apparatus for generating high frequencyorders, and infer the data associated with the orders in the targetexchange server in response to the input of data received from thecommunication module and/or memory.

According to an embodiment, the market prediction reference data of anartificial neural network model for inferring data associated with theorders in the target exchange server may include one or more referencefeatures for one or more items at one or more time points. For example,the market prediction reference data input to the input layer 1120 ofthe artificial neural network model 1100 may be a vector 1110 in whichdata including information on one or more reference features for one ormore items at one or more time points is configured as one vector dataelement. In response to the input of data, future market prediction datareceived from the output layer 1140 of the artificial neural networkmodel 1100 may be a vector 1150 representing or characterizing the dataassociated with the order in the target exchange server. That is, theoutput layer 1140 of the artificial neural network model 1100 may beconfigured to output a vector representing or characterizing the dataassociated with the order in the target exchange server. The futuremarket prediction data that the artificial neural network model 1100outputs is not limited to the type described above, and may include anyinformation/data representing data associated with the order in thetarget exchange server.

As described above, the input layer 1120 and the output layer 1140 ofthe artificial neural network model 1100 are respectively matched with aplurality of future market prediction data corresponding to a pluralityof market prediction reference data, and the synaptic values betweennodes included in the input layer 1120, and the hidden layers 1130_1 to1130_n, and the output layer 1140 are adjusted, so that training can beprocessed to extract a correct output corresponding to a specific input.Through this training process, the features hidden in the marketprediction reference data corresponding to the input data of theartificial neural network model 1100 may be confirmed, and the synapticvalues (or weights) between the nodes of the artificial neural networkmodel 1100 may be adjusted so as to reduce the errors between the futuremarket prediction data calculated based on the market predictionreference data and the target output. The artificial neural networkmodel 1100 trained as described above may output the data associatedwith the order in the target exchange server in response to the marketprediction reference data.

FIG. 12 is a block diagram of any computing device 1200 associated withhigh frequency trading or generation of high frequency orders accordingto an embodiment of the present disclosure. For example, the computingdevice 1200 may include the information processing system 120 and/or theuser terminal 130. As illustrated, the computing device 1200 may includeone or more processors 1210, a bus 1230, a communication interface 1240,a memory 1220 for loading a computer program 1260 to be executed by theprocessors 1210, and a storage module 1250 for storing the computerprogram 1260. Meanwhile, only the components related to the embodimentare illustrated in FIG. 12 . Accordingly, those of ordinary skill in theart to which the present disclosure pertains will be able to recognizethat other general-purpose components may be further included inaddition to the components illustrated in FIG. 12 .

The processors 1210 control the overall operation of each component ofthe computing device 1200. The processor 1210 may include centralprocessing unit (CPU), micro processor unit (MPU), micro controller unit(MCU), graphic processing unit (GPU), neural processing unit (NPU), orany type of processor well known in the technical field of the presentdisclosure. In addition, the processors 1210 may perform an arithmeticoperation on at least one application or program for executing themethod according to the embodiments of the present disclosure. Thecomputing device 1200 may include one or more processors. For example,the computing device 1200 may include a processor implemented in an FPGA302, and a dedicated accelerator for a machine learning modelimplemented in an ASIC (NPU ASIC).

The memory 1220 may store various types of data, commands, and/orinformation. The memory 1220 may load one or more computer programs 1260from the storage module 1250 in order to execute the method/operationaccording to various embodiments of the present disclosure. The memory1220 may be implemented as a volatile memory such as RAM, although thetechnical scope of the present disclosure is not limited thereto.

The bus 1230 may provide a communication function between components ofthe computing device 1200. The bus 1230 may be implemented as varioustypes of buses such as an address bus, a data bus, a control bus, or thelike.

The communication interface 1240 may support wired/wireless Internetcommunication of the computing device 1200. In addition, thecommunication interface 1240 may support various other communicationmethods in addition to the Internet communication. To this end, thecommunication interface 1240 may include a communication module wellknown in the technical field of the present disclosure.

The storage module 1250 may non-temporarily store one or more computerprograms 1260. The storage module 1250 may include a nonvolatile memorysuch as a read only memory (ROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a flash memory,etc., a hard disk, a detachable disk, or any type of computer-readablerecording medium well known in the art to which the present disclosurepertains.

The computer program 1260 may include one or more instructions that, ifloaded into the memory 1220, cause the processors 1210 to perform anoperation/method in accordance with various embodiments of the presentdisclosure. That is, the processors 1210 may perform operations/methodsaccording to various embodiments of the present disclosure by executingone or more instructions.

For example, the computer program 1260 may include instructions forgenerating the market prediction reference data based on themarket-related information collected from the one or more market-relatedinformation servers 120, generating the future market prediction dataassociated with the order for the target exchange from the machinelearning model based on the generated market prediction reference data,and generating the orders for the target item of the target exchangebased on the future market prediction data. As another example, thecomputer program 1260 may include instructions for receiving, by theFPGA 302, first market data of the first exchange and second market dataof the second exchange, generating, by the FPGA 302, market predictionreference data as the input data of the machine learning model based onat least one of the first market data or the second market data,processing, by the NPU 340, at least some operations for the machinelearning model, providing future market prediction data output from themachine learning model to the FPGA 302, and generating, by the FPGA 302,the orders in the target exchange based on the future market predictiondata received from the machine learning model.

FIG. 13 is a ladder diagram illustrating an example signal exchangeaccording to an embodiment of the present disclosure.

At S1301, the FPGA 302 may collect market-related information from oneor more market-related information servers 120.

At S1303, the FPGA 302 may generate market prediction reference databased on the collected market-related information.

At S1305, the FPGA 302 may determine an appropriate way for predicting afuture market based on the market prediction reference data. In someembodiments, the FPGA 302 may determine a prediction complexity based onthe market prediction reference data, and then determine the appropriateway based on the prediction complexity. In some embodiments, the FPGA302 may determine a prediction complexity between a low complexity and ahigh complexity or between a low complexity, a moderate complexity, anda high complexity. In some embodiments, the FPGA 302 may determine theappropriate way between the rule-based logic 532 and a machine learningmodel, or between the rule-based logic 532, a relatively light machinelearning model, and a relatively heavy machine learning model. In someembodiments, the FPGA 302 may determine the rule-based logic 532, therelatively light machine learning model, and the relatively heavymachine learning model as the appropriate way when the predictioncomplexity is the low complexity, the moderate complexity, and the highcomplexity, respectively.

At S1307, if the FPGA 302 determines that the appropriate way is therelatively heavy machine learning model, the FPGA 302 may transmit themarket prediction reference data to the NPU 340. In some embodiments,the FPGA 302 may transmit the market prediction reference data to theNPU 340 so that the NPU 340 may generate future market prediction dataregardless of whether the FPGA 302 determines that that the appropriateway is the relatively heavy machine learning model.

At S1309, the NPU 340 may preform operations for the relatively heavymachine learning model with the market prediction reference dataaccording to the machine learning model to generate the future marketprediction data.

At S1311, the NPU 340 may transmit the future market prediction data tothe FPGA 302.

At S1313, if the FPGA 302 determines that the appropriate way is therule-based logic 532, the FPGA 302 may perform the rule-based logic 532with the market prediction reference data to generate future marketprediction data. In some embodiments, the FPGA 302 may generate an ordersignal without generating future market prediction data when the FPGA302 determines that the appropriate way is the rule-based logic 532.

At S1315, if the FPGA 302 determines that the appropriate way is therelatively light machine learning model, the FPGA 302 may transmit themarket prediction reference data to the host device 440. In someembodiments, the FPGA 302 may transmit the market prediction referencedata to the host device 440 so that the host device 440 may generatefuture market prediction data regardless of whether the FPGA 302determines that the appropriate way is the relatively light machinelearning model.

At S1317, the host device 440 may generate the future market predictiondata with the market prediction reference data. In some embodiments, thehost device 440 may use a rule-based logic which is heavier than therule-based logic 532 used by the FPGA 302. In some embodiments, the hostdevice 440 may use a machine learning model which is lighter than themachine learning model used by the NPU 340.

At S1319, the host device 440 may transmit the future market predictiondata to the FPGA 302.

At S1321, the FPGA 302 may generate an order signal based on the futuremarket prediction data received from at least one of the FPGA 302, theNPU 340, or host device 440.

At 51323, the FPGA 302 may transmit an order signal to the targetexchange server 130.

The method described above may be provided as a computer program storedin a computer-readable recording medium for execution on a computer. Themedium may be a type of medium that continuously stores a programexecutable by a computer, or temporarily stores the program forexecution or download. In addition, the medium may be a variety ofrecording means or storage means having a single piece of hardware or acombination of several pieces of hardware, and is not limited to amedium that is directly connected to any computer system, andaccordingly, may be present on a network in a distributed manner. Anexample of the medium includes a medium configured to store programinstructions, including a magnetic medium such as a hard disk, a floppydisk, and a magnetic tape, an optical medium such as a CD-ROM and a DVD,a magnetic-optical medium such as a floptical disk, and a ROM, a RAM, aflash memory, and so on. In addition, other examples of the medium mayinclude an app store that distributes applications, a site that suppliesor distributes various software, and a recording medium or a storagemedium managed by a server.

The methods, operations, or techniques of the present disclosure may beimplemented by various means. For example, these techniques may beimplemented in hardware, firmware, software, or a combination thereof.Those skilled in the art will further appreciate that variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the disclosure herein may be implemented inelectronic hardware, computer software, or combinations of both. Toclearly illustrate this interchangeability of hardware and software,various illustrative components, blocks, modules, circuits, and stepshave been described above generally in terms of their functionality.Whether such a function is implemented as hardware or software variesdepending on design requirements imposed on the particular applicationand the overall system. Those skilled in the art may implement thedescribed functions in varying ways for each particular application, butsuch implementation should not be interpreted as causing a departurefrom the scope of the present disclosure.

In a hardware implementation, processing units used to perform thetechniques may be implemented in one or more ASICs, DSPs, digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), FPGAs,processors, controllers, microcontrollers, microprocessors, electronicdevices, other electronic units designed to perform the functionsdescribed in the present disclosure, computer, or a combination thereof.

Accordingly, various example logic blocks, modules, and circuitsdescribed in connection with the present disclosure may be implementedor performed with general purpose processors, DSPs, ASICs, FPGAs orother programmable logic devices, discrete gate or transistor logic,discrete hardware components, or any combination of those designed toperform the functions described herein. The general purpose processormay be a microprocessor, but in the alternative, the processor may beany related processor, controller, microcontroller, or state machine.The processor may also be implemented as a combination of computingdevices, for example, a DSP and microprocessor, a plurality ofmicroprocessors, one or more microprocessors associated with a DSP core,or any other combination of the configurations.

In the implementation using firmware and/or software, the techniques maybe implemented with instructions stored on a computer-readable medium,such as random access memory (RAM), read-only memory (ROM), non-volatilerandom access memory (NVRAM), programmable read-only memory (PROM),erasable programmable read-only memory (EPROM), electrically erasablePROM (EEPROM), flash memory, compact disc (CD), magnetic or optical datastorage devices, etc. The instructions may be executable by one or moreprocessors, and may cause the processor(s) to perform certain aspects ofthe functions described in the present disclosure.

If implemented in software, the techniques may be stored on acomputer-readable medium as one or more instructions or codes, or may betransmitted through a computer-readable medium. The computer-readablemedia include both the computer storage media and the communicationmedia including any medium that facilitates the transfer of a computerprogram from one place to another. The storage media may also be anyavailable media that may be accessed by a computer. By way ofnon-limiting example, such a computer-readable medium may include RAM,ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storageor other magnetic storage devices, or any other media that can be usedto transfer or store desired program code in the form of instructions ordata structures and can be accessed by a computer. In addition, anyconnection is properly referred to as a computer-readable medium.

For example, if the software is transmitted from a website, server, orother remote sources using coaxial cable, fiber optic cable, twistedpair, digital subscriber line (DSL), or wireless technologies such asinfrared, wireless, and microwave, the coaxial cable, the fiber opticcable, the twisted pair, the digital subscriber line, or the wirelesstechnologies such as infrared, wireless, and microwave are includedwithin the definition of the medium. The disks and the discs used hereininclude CDs, laser disks, optical disks, digital versatile discs (DVDs),floppy disks, and Blu-ray disks, where disks usually magneticallyreproduce data, while discs optically reproduce data using a laser. Thecombinations described above should also be included within the scope ofthe computer-readable media.

The software module may reside in RAM memory, flash memory, ROM memory,EPROM memory, EEPROM memory, registers, hard disk, removable disk,CD-ROM, or any other form of storage medium known. An exemplary storagemedium may be connected to the processor, such that the processor mayread or write information from or to the storage medium. Alternatively,the storage medium may be integrated into the processor. The processorand the storage medium may exist in the ASIC. The ASIC may exist in theuser terminal. Alternatively, the processor and storage medium may existas separate components in the user terminal.

Although the embodiments described above have been described asutilizing aspects of the currently disclosed subject matter in one ormore standalone computer systems, the present disclosure is not limitedthereto, and may be implemented in conjunction with any computingenvironment, such as a network or distributed computing environment.Furthermore, the aspects of the subject matter in the present disclosuremay be implemented in multiple processing chips or devices, and storagemay be similarly influenced across a plurality of devices. Such devicesmay include PCs, network servers, and portable devices.

Although the present disclosure has been described in connection withsome embodiments herein, various modifications and changes can be madewithout departing from the scope of the present disclosure, which can beunderstood by those skilled in the art to which the present disclosurepertains. In addition, such modifications and changes should beconsidered within the scope of the claims appended herein.

1. An apparatus for high frequency trading, comprising: one or morememories; at least one reconfigurable processor coupled to the one ormore memories, the one or more processors configured to receivemarket-related information from one or more market-related informationservers and generate market prediction reference data based on themarket-related information; and a dedicated accelerator preconfiguredfor the machine learning model, the dedicated accelerator configured toreceive the market prediction reference data, perform operations for themachine learning model with the market prediction reference data togenerate future market prediction data, and provide the future marketprediction data to the at least one reconfigurable processor, whereinthe at least one reconfigurable processor is further configured togenerate an order signal based on the future market prediction data andtransmit the order signal to a target exchange server.
 2. The apparatusaccording to claim 1, wherein the at least one reconfigurable processoris implemented as a field programmable gate array (FPGA), and thededicated accelerator is implemented as an integrated circuit for aneural processing unit (NPU).
 3. The apparatus according to claim 1,wherein the one or more market-related information servers includes atleast one of one or more reference exchange servers, one or more newsproviding servers, one or more social network service (SNS) servers, orone or more online shopping service servers.
 4. The apparatus accordingto claim 1, wherein the at least one reconfigurable processor is furtherconfigured to determine a prediction complexity based on the marketprediction reference data, and determine an appropriate way according tothe determined prediction complexity, if it is determined that theappropriate way is a predetermined rule, the at least one reconfigurableprocessor is further configured to generate the order signal accordingto the predetermined rule based on the market prediction reference data,and if it is determined that the appropriate way is the machine learningmodel, the dedicated accelerator is further configured to performoperations for the machine learning model to generate the future marketprediction data and provide the future market prediction data to the atleast one reconfigurable processor so that the at least onereconfigurable processor generates the order signal based on the futuremarket prediction data.
 5. The apparatus according to claim 4, furthercomprising a host device configured to drive a trading engine, whereinthe prediction complexity includes three complexity classes according tothe complexity, and the market prediction reference data is provided toat least one of the at least one reconfigurable processor, the hostdevice or the dedicated accelerator for the machine learning modelaccording to the three complexity classes to generate the order signal.6. The apparatus according to claim 1, wherein the at least onereconfigurable processor is further configured to parse and decode themarket-related information, and generate the market prediction referencedata based on the parsed and decoded market-related information.
 7. Theapparatus according to claim 1, wherein the market prediction referencedata includes one or more reference features for one or more referenceitems at one or more time points.
 8. The apparatus according to claim 7,wherein the one or more reference items include a reference itemrepresenting a leading indicator, and a target item to be ordered. 9.The apparatus according to claim 1, wherein the at least onereconfigurable processor is further configured to process the generatedorder signal according to a protocol required by the target exchangeserver.
 10. The apparatus according to claim 1, wherein themarket-related information includes information on an order book of oneor more reference items in a reference exchange associated with areference exchange server, and a response to a previous order in thetarget exchange associated with the target exchange server.
 11. A methodof operating an apparatus for high frequency trading including at leastone reconfigurable processor, the method comprising: receiving, by theat least one reconfigurable processor, market-related information fromone or more market-related information servers; generating, by the atleast one reconfigurable processor, market prediction reference databased on the market-related information; transmitting, by the at leastone reconfigurable processor, the market prediction reference data to adedicated accelerator preconfigured for the machine learning model andconfigured to perform operations of the machine learning model with themarket prediction reference data to generate future market predictiondata; receiving, by the at least one reconfigurable processor, thefuture market prediction data from the dedicated accelerator;generating, by the at least one reconfigurable processor, an ordersignal based on the future market prediction data; and transmitting theorder signal to a target exchange server.
 12. The method according toclaim 11, wherein the one or more market-related information serversincludes at least one of one or more reference exchange servers, one ormore news providing servers, one or more social network service (SNS)servers, or one or more online shopping service servers.
 13. The methodaccording to claim 11, further comprising: determining, by the at leastone reconfigurable processor, a prediction complexity based on themarket prediction reference data; determining, by the at least onereconfigurable processor, an appropriate way according to the determinedprediction complexity; if it is determined that the appropriate way is apredetermined rule, generating, by the at least one reconfigurableprocessor, the order signal according to the predetermined rule based onthe market prediction reference data; and if it is determined that theappropriate way is the machine learning model, transmitting, by the atleast one reconfigurable processor, the market prediction reference datato the dedicated accelerator configured to perform operations of themachine learning model operations for the machine learning model withthe market prediction reference data to generate future marketprediction data.
 14. The method according to claim 13, wherein theprediction complexity includes three complexity classes according to thecomplexity, and the market prediction reference data is provided to atleast one of the at least one reconfigurable processor, a host device orthe NPU according to the three complexity classes to generate the ordersignal.
 15. The method according to claim 11, further comprising:parsing and decoding, by the at least one reconfigurable processor, themarket-related information, and wherein generating the market predictionreference data comprises: generating the market prediction referencedata based on the parsed and decoded market-related information.
 16. Themethod according to claim 11, wherein the market prediction referencedata includes one or more reference features for one or more referenceitems at one or more time points.
 17. The method according to claim 16,wherein the one or more reference items include a reference itemrepresenting a leading indicator, and a target item to be ordered. 18.The method according to claim 11, further comprising: processing, by theat least one reconfigurable processor, the generated order signalaccording to a protocol required by the target exchange server.
 19. Themethod according to claim 11, wherein the market-related informationincludes information on an order book of one or more reference items ina reference exchange associated with a reference exchange server, and aresponse to a previous order in the target exchange associated with thetarget exchange server.
 20. An apparatus for high frequency trading,comprising: one or more memories; at least one reconfigurable processorcoupled to the one or more memories, the one or more processorsconfigured to cause: receiving market-related information from one ormore market-related information servers; generating market predictionreference data based on the market-related information; transmitting themarket prediction reference data to a dedicated acceleratorpreconfigured for the machine learning model and configured to performoperations of the machine learning model with the market predictionreference data to generate future market prediction data; receiving thefuture market prediction data from the dedicated accelerator; generatingan order signal based on the future market prediction data; andtransmitting the order signal to a target exchange server.